public_space_app.py

from __future__ import annotations

import json
from pathlib import Path

import gradio as gr
import pandas as pd
import plotly.express as px


EUROPE_COUNTRIES = {
    "Belgium",
    "Denmark",
    "Germany",
    "Ireland",
    "Italy",
    "Netherlands",
    "Spain",
    "Sweden",
    "United Kingdom",
}
CLARITY_LABELS = {
    "resolved_sensitive_site_report": "Specific site matched",
    "named_sensitive_site_report": "Specific site named",
    "source_discovered_report": "News lead to review",
}
LOCATION_LABELS = {
    "site_centroid": "Specific site location",
    "city_area_centroid": "City-area location",
    "region_centroid": "General regional location",
    "country_centroid": "Country-level location",
}
STORY_CHOICES = [
    "Start here: main storylines",
    "New Jersey coastal/security reports",
    "European airport disruptions",
    "Military base reports",
    "All reports by place",
]
REPORT_COLUMNS = [
    "Headline",
    "Date",
    "Place",
    "Place type",
    "Country",
    "Source",
    "Why included",
    "Caution",
]
PLACE_COLUMNS = [
    "Place",
    "Reports",
    "Place type",
    "Region",
    "Location note",
    "Date span",
    "Why look here",
]
TECH_COLUMNS = [
    "case_id",
    "case_rank",
    "evidence_tier",
    "coordinate_quality",
    "probable_cluster_id",
    "public_row_sha256",
]


def _load_data(data_dir: Path) -> tuple[pd.DataFrame, dict, dict]:
    cases = pd.read_csv(data_dir / "mystery_drone_sensitive_site_cases.csv").fillna("")
    manifest = json.loads((data_dir / "release_manifest.json").read_text(encoding="utf-8"))
    quality = json.loads((data_dir / "quality_report.json").read_text(encoding="utf-8"))
    cases["case_rank"] = pd.to_numeric(cases["case_rank"], errors="coerce").fillna(999999).astype(int)
    cases["plot_lat"] = pd.to_numeric(cases["plot_lat"], errors="coerce")
    cases["plot_lon"] = pd.to_numeric(cases["plot_lon"], errors="coerce")
    cases["report_year"] = cases["report_date"].astype(str).str.slice(0, 4).replace("", "Older / unknown")
    cases["reader_clarity"] = cases["evidence_tier"].map(CLARITY_LABELS).fillna("News lead to review")
    cases["location_note"] = cases["coordinate_quality"].map(LOCATION_LABELS).fillna("General location")
    cases["place_type_reader"] = cases.apply(_place_type_label, axis=1)
    cases["region_reader"] = cases["country"].map(_region_label)
    cases["story_group"] = cases.apply(_story_group, axis=1)
    cases["reader_caution"] = cases.apply(_reader_caution, axis=1)
    cases["why_included"] = cases.apply(_why_included, axis=1)
    cases["map_group_id"] = cases.apply(
        lambda row: "|".join(
            [
                f"{float(row['plot_lat']):.4f}" if pd.notna(row["plot_lat"]) else "",
                f"{float(row['plot_lon']):.4f}" if pd.notna(row["plot_lon"]) else "",
                str(row.get("plot_label", "")),
                str(row.get("place_type_reader", "")),
                str(row.get("country", "")),
            ]
        ),
        axis=1,
    )
    return cases, manifest, quality


def _place_type_label(row: pd.Series) -> str:
    text = f"{row.get('site_type', '')} {row.get('site_name', '')} {row.get('plot_label', '')} {row.get('headline', '')}".lower()
    if "airport" in text or "runway" in text:
        return "Airport"
    if "coast guard" in text or "coastal" in text or "maritime" in text or "new jersey" in text:
        return "Coastal/security"
    if "military" in text or "air force" in text or "air base" in text or "arsenal" in text or "raf " in text or "joint base" in text:
        return "Military site"
    if "critical" in text or "infrastructure" in text or "nuclear" in text or "power" in text:
        return "Critical infrastructure"
    return "Other / unclear"


def _region_label(country: str) -> str:
    if country == "United States":
        return "United States"
    if country in EUROPE_COUNTRIES:
        return "Europe"
    return "Other / unclear"


def _story_group(row: pd.Series) -> str:
    text = f"{row.get('headline', '')} {row.get('site_name', '')} {row.get('plot_label', '')} {row.get('country', '')}".lower()
    if "new jersey" in text or "coast guard" in text:
        return "New Jersey coastal/security reports"
    if row.get("region_reader") == "Europe" and ("airport" in text or row.get("place_type_reader") == "Airport"):
        return "European airport disruptions"
    if row.get("place_type_reader") == "Military site":
        return "Military base reports"
    return "All reports by place"


def _reader_caution(row: pd.Series) -> str:
    clarity = row.get("reader_clarity", "")
    location = row.get("location_note", "")
    if clarity == "News lead to review":
        return "Treat as a source lead, not a confirmed event."
    if location != "Specific site location":
        return "Location is approximate."
    return "Check the linked source before drawing conclusions."


def _why_included(row: pd.Series) -> str:
    clarity = row.get("reader_clarity", "")
    place_type = row.get("place_type_reader", "")
    if clarity == "Specific site matched":
        return f"Matched to a {place_type.lower()} report location."
    if clarity == "Specific site named":
        return f"The source names a {place_type.lower()} or sensitive place."
    return f"The source language points to a drone report near a {place_type.lower()} context."


def _date_span(values: pd.Series) -> str:
    dates = sorted(str(value) for value in values if str(value))
    if not dates:
        return "Date unclear"
    if dates[0] == dates[-1]:
        return dates[0]
    return f"{dates[0]} to {dates[-1]}"


def _count_text(values: pd.Series, limit: int = 4) -> str:
    counts = values.astype(str).replace("", "unknown").value_counts()
    return ", ".join(f"{key}: {int(value)}" for key, value in counts.head(limit).items())


def _header(manifest: dict) -> str:
    named_or_matched = int(manifest.get("resolved_sensitive_site_report_count", 0)) + int(
        manifest.get("named_sensitive_site_report_count", 0)
    )
    leads = int(manifest.get("source_discovered_report_count", 0))
    return f"""# Mystery Drone Reports Near Sensitive Places

This is a public-source index of news reports near airports, military sites, coastal/security areas, and other sensitive places. It is not proof of threat, intent, or unusual origin.

**{manifest.get("case_count", 0)} public-source reports** | **{named_or_matched} name or match a specific sensitive site** | **{leads} broader leads for follow-up**
"""


def _story_intro(story: str, rows: pd.DataFrame) -> str:
    if rows.empty:
        return "No reports match this storyline."
    places = _count_text(rows["plot_label"], limit=5)
    sources = _count_text(rows["source_domain"], limit=5)
    dates = _date_span(rows["report_date"])
    location_note = "Some markers are approximate because public reports often describe areas rather than exact coordinates."
    if story == "New Jersey coastal/security reports":
        lead = "This group collects public reports connected to the New Jersey drone wave and nearby coastal/security locations."
        caution = "Many rows are broad reporting leads, so treat this as a reporting trail rather than a confirmed incident list."
    elif story == "European airport disruptions":
        lead = "This group follows reports around European airport disruptions and related drone activity."
        caution = "Airport closures and disruption reports can involve repeated follow-up stories, so use the source links to separate event reports from later context."
    elif story == "Military base reports":
        lead = "This group focuses on reports that name or point toward military bases and military-site areas."
        caution = "A report near a base does not prove origin, intent, or threat."
    elif story == "All reports by place":
        lead = "This view groups the full report set by place so repeated locations are easier to scan."
        caution = "Marker size means number of source reports, not number of confirmed objects."
    else:
        lead = "Pick a storyline below to explore the main reporting trails."
        caution = "Start with the story summaries, then use the map and sources for details."
    return f"""## {story}

{lead}

- Reports in view: **{len(rows)}**
- Date range: **{dates}**
- Common places: {places}
- Common sources: {sources}

**What this does not prove:** {caution}

**Location note:** {location_note}
"""


def _story_rows(cases: pd.DataFrame, story: str) -> pd.DataFrame:
    if story == "Start here: main storylines":
        return cases.copy()
    if story == "All reports by place":
        return cases.copy()
    return cases[cases["story_group"] == story].copy()


def _filter_rows(cases: pd.DataFrame, search: str, region: str, place_type: str, clarity: str, year: str) -> pd.DataFrame:
    rows = cases.copy()
    if region and region != "All":
        rows = rows[rows["region_reader"] == region]
    if place_type and place_type != "All":
        rows = rows[rows["place_type_reader"] == place_type]
    if clarity and clarity != "All":
        rows = rows[rows["reader_clarity"] == clarity]
    if year and year != "All":
        if year == "Older / unknown":
            rows = rows[~rows["report_year"].isin(["2024", "2025", "2026"])]
        else:
            rows = rows[rows["report_year"] == year]
    search = str(search or "").strip().lower()
    if search:
        haystack = (
            rows["headline"].astype(str)
            + " "
            + rows["site_name"].astype(str)
            + " "
            + rows["plot_label"].astype(str)
            + " "
            + rows["country"].astype(str)
            + " "
            + rows["source_domain"].astype(str)
        ).str.lower()
        rows = rows[haystack.str.contains(search, regex=False)]
    return rows.sort_values(["case_rank"]).reset_index(drop=True)


def _group_rows(rows: pd.DataFrame) -> pd.DataFrame:
    out: list[dict] = []
    if rows.empty:
        return pd.DataFrame(columns=["Place", "Reports", "Place type", "Region", "Location note", "Date span", "Why look here", "map_group_id", "plot_lat", "plot_lon"])
    for group_id, group in rows.groupby("map_group_id", sort=False):
        out.append(
            {
                "map_group_id": group_id,
                "Place": str(group["plot_label"].iloc[0]),
                "Reports": int(len(group)),
                "Place type": str(group["place_type_reader"].iloc[0]),
                "Region": str(group["region_reader"].iloc[0]),
                "Location note": str(group["location_note"].iloc[0]),
                "Date span": _date_span(group["report_date"]),
                "Why look here": _count_text(group["reader_clarity"], limit=3),
                "plot_lat": float(group["plot_lat"].iloc[0]),
                "plot_lon": float(group["plot_lon"].iloc[0]),
                "source_summary": _count_text(group["source_domain"], limit=3),
            }
        )
    grouped = pd.DataFrame(out)
    return grouped.sort_values(["Reports", "Place"], ascending=[False, True]).reset_index(drop=True)


def _map(groups: pd.DataFrame):
    if groups.empty:
        fig = px.scatter_geo(pd.DataFrame({"plot_lat": [], "plot_lon": []}), lat="plot_lat", lon="plot_lon", height=560)
        fig.update_layout(margin={"l": 0, "r": 0, "t": 12, "b": 0})
        return fig
    fig = px.scatter_geo(
        groups,
        lat="plot_lat",
        lon="plot_lon",
        color="Place type",
        size="Reports",
        size_max=38,
        hover_name="Place",
        hover_data={
            "Reports": True,
            "Region": True,
            "Location note": True,
            "Date span": True,
            "Why look here": True,
            "source_summary": True,
            "plot_lat": False,
            "plot_lon": False,
        },
        projection="natural earth",
        height=560,
        color_discrete_map={
            "Airport": "#1f77b4",
            "Military site": "#b42318",
            "Coastal/security": "#2e7d62",
            "Critical infrastructure": "#8e5ea2",
            "Other / unclear": "#6b7280",
        },
    )
    fig.update_traces(marker={"opacity": 0.8, "line": {"width": 0.6, "color": "white"}})
    fig.update_geos(showland=True, landcolor="#eef2f5", showocean=True, oceancolor="#dfeaf2", showcountries=True)
    fig.update_layout(margin={"l": 0, "r": 0, "t": 18, "b": 0}, legend_title_text="Place type")
    return fig


def _public_table(rows: pd.DataFrame) -> pd.DataFrame:
    if rows.empty:
        return pd.DataFrame(columns=REPORT_COLUMNS)
    return pd.DataFrame(
        {
            "Headline": rows["headline"],
            "Date": rows["report_date"].replace("", "Date unclear"),
            "Place": rows["plot_label"],
            "Place type": rows["place_type_reader"],
            "Country": rows["country"].replace("", "unknown"),
            "Source": rows["source_domain"],
            "Why included": rows["why_included"],
            "Caution": rows["reader_caution"],
        }
    )


def _source_cards(rows: pd.DataFrame, limit: int = 10) -> str:
    if rows.empty:
        return "No reports match this view."
    lines = ["## Source links to inspect", ""]
    for _, row in rows.head(limit).iterrows():
        lines.extend(
            [
                f"### {row['headline']}",
                f"- Date: {row['report_date'] or 'Date unclear'}",
                f"- Place: {row['plot_label']} ({row['location_note']})",
                f"- Why included: {row['why_included']}",
                f"- Caution: {row['reader_caution']}",
                f"- Source: [{row['publisher'] or row['source_domain']}]({row['source_url']})",
                "",
            ]
        )
    if len(rows) > limit:
        lines.append(f"...and {len(rows) - limit} more reports in the list.")
    return "\n".join(lines)


def _story_card_markdown(cases: pd.DataFrame) -> str:
    cards = []
    for story in STORY_CHOICES[1:]:
        rows = _story_rows(cases, story)
        if story == "All reports by place":
            subtitle = "Scan every mapped report grouped by place."
        elif story == "New Jersey coastal/security reports":
            subtitle = "The largest reporting trail in this release."
        elif story == "European airport disruptions":
            subtitle = "Airport closures and disruption reports across Europe."
        else:
            subtitle = "Reports around bases and military-site areas."
        cards.append(f"**{story}** - {len(rows)} reports. {subtitle}")
    return "## Pick a storyline to explore\n\n" + "\n\n".join(cards)


def _render_story(cases: pd.DataFrame, story: str):
    rows = _story_rows(cases, story)
    groups = _group_rows(rows)
    intro = _header_from_rows(cases) + "\n\n" + _story_card_markdown(cases) if story == "Start here: main storylines" else _story_intro(story, rows)
    return intro, _map(groups), groups[PLACE_COLUMNS], _public_table(rows), _source_cards(rows)


def _header_from_rows(cases: pd.DataFrame) -> str:
    specific = int((cases["reader_clarity"].isin(["Specific site matched", "Specific site named"])).sum())
    leads = int((cases["reader_clarity"] == "News lead to review").sum())
    return f"""# Mystery Drone Reports Near Sensitive Places

This is a public-source index of news reports near airports, military sites, coastal/security areas, and other sensitive places.

It is not proof of threat, intent, or unusual origin.

**{len(cases)} public-source reports** | **{specific} name or match a specific sensitive site** | **{leads} broader leads for follow-up**
"""


def _render_map(cases: pd.DataFrame, search: str, region: str, place_type: str, clarity: str, year: str):
    rows = _filter_rows(cases, search, region, place_type, clarity, year)
    groups = _group_rows(rows)
    summary = (
        f"Showing {len(rows)} reports at {len(groups)} places. "
        "Bigger markers mean more reports at that place. Colors show the kind of place."
    )
    return summary, _map(groups), groups[PLACE_COLUMNS], _public_table(rows), _source_cards(rows)


def _render_reports(cases: pd.DataFrame, search: str, region: str, place_type: str, clarity: str, year: str):
    rows = _filter_rows(cases, search, region, place_type, clarity, year)
    summary = f"Showing {len(rows)} reports. Select a row by using the source links in the detail panel below."
    return summary, _public_table(rows), _source_cards(rows), _technical_table(rows)


def _technical_table(rows: pd.DataFrame) -> pd.DataFrame:
    if rows.empty:
        return pd.DataFrame(columns=TECH_COLUMNS)
    return rows[TECH_COLUMNS].copy()


def _data_notes(manifest: dict, quality: dict) -> str:
    return f"""# Data notes

This Space keeps the technical classifications available, but keeps them out of the first screen.

- Release version: {manifest.get('release_version')}
- Public rows: {manifest.get('case_count')}
- Quality gate passed: {quality.get('release_grade')}
- Duplicate source URLs: {quality.get('duplicate_source_url_count')}
- Missing source URLs: {quality.get('missing_source_url_count')}
- Mappable rows: {quality.get('mappable_case_count')}

Plain-language translations:

- Specific site matched = stricter source/site matching found a sensitive-site report.
- Specific site named = the source names a sensitive site, but it still needs review.
- News lead to review = public source language suggests a relevant report, but this is a lead, not a confirmed event.
- Specific site location = marker uses a known site point.
- General regional location or country-level location = marker is approximate.
"""


def build_app(data_dir: str | Path):
    data_dir = Path(data_dir)
    cases, manifest, quality = _load_data(data_dir)
    region_choices = ["All", "United States", "Europe", "Other / unclear"]
    place_choices = ["All", "Airport", "Military site", "Coastal/security", "Critical infrastructure", "Other / unclear"]
    clarity_choices = ["All", "Specific site matched", "Specific site named", "News lead to review"]
    year_choices = ["All", "2026", "2025", "2024", "Older / unknown"]

    with gr.Blocks(title="Mystery Drone Reports Near Sensitive Places") as app:
        with gr.Tab("Start here"):
            story = gr.Radio(choices=STORY_CHOICES, value=STORY_CHOICES[0], label="Pick a storyline")
            story_intro = gr.Markdown()
            with gr.Row():
                story_map = gr.Plot(label="Story map")
                story_sources = gr.Markdown()
            story_places = gr.Dataframe(label="Places in this story", interactive=False)
            story_reports = gr.Dataframe(label="Reports in this story", interactive=False)
            story.change(
                lambda selected: _render_story(cases, selected),
                inputs=story,
                outputs=[story_intro, story_map, story_places, story_reports, story_sources],
            )
            app.load(
                lambda: _render_story(cases, STORY_CHOICES[0]),
                outputs=[story_intro, story_map, story_places, story_reports, story_sources],
            )

        with gr.Tab("Map"):
            gr.Markdown("## Map\n\nBigger markers mean more public-source reports at that place. Colors show the kind of place.")
            with gr.Row():
                map_search = gr.Textbox(label="Search", placeholder="Search a place, country, source, or headline")
                map_region = gr.Dropdown(choices=region_choices, value="All", label="Region")
                map_place = gr.Dropdown(choices=place_choices, value="All", label="Place type")
                map_clarity = gr.Dropdown(choices=clarity_choices, value="All", label="Report clarity")
                map_year = gr.Dropdown(choices=year_choices, value="All", label="Time")
            map_summary = gr.Markdown()
            map_plot = gr.Plot(label="Report map")
            map_places = gr.Dataframe(label="Places shown on the map", interactive=False)
            map_reports = gr.Dataframe(label="Reports shown by current filters", interactive=False)
            map_sources = gr.Markdown()
            map_inputs = [map_search, map_region, map_place, map_clarity, map_year]
            for control in map_inputs:
                control.change(
                    lambda search, region, place, clarity, year: _render_map(cases, search, region, place, clarity, year),
                    inputs=map_inputs,
                    outputs=[map_summary, map_plot, map_places, map_reports, map_sources],
                )
            app.load(
                lambda: _render_map(cases, "", "All", "All", "All", "All"),
                outputs=[map_summary, map_plot, map_places, map_reports, map_sources],
            )

        with gr.Tab("Reports"):
            gr.Markdown("## All reports\n\nUse this when you want source links and row-level cautions.")
            with gr.Row():
                report_search = gr.Textbox(label="Search", placeholder="Search a place, country, source, or headline")
                report_region = gr.Dropdown(choices=region_choices, value="All", label="Region")
                report_place = gr.Dropdown(choices=place_choices, value="All", label="Place type")
                report_clarity = gr.Dropdown(choices=clarity_choices, value="All", label="Report clarity")
                report_year = gr.Dropdown(choices=year_choices, value="All", label="Time")
            report_summary = gr.Markdown()
            report_table = gr.Dataframe(label="Readable report list", interactive=False)
            report_sources = gr.Markdown()
            with gr.Accordion("Show technical fields", open=False):
                technical_table = gr.Dataframe(label="Technical row fields", interactive=False)
            report_inputs = [report_search, report_region, report_place, report_clarity, report_year]
            for control in report_inputs:
                control.change(
                    lambda search, region, place, clarity, year: _render_reports(cases, search, region, place, clarity, year),
                    inputs=report_inputs,
                    outputs=[report_summary, report_table, report_sources, technical_table],
                )
            app.load(
                lambda: _render_reports(cases, "", "All", "All", "All", "All"),
                outputs=[report_summary, report_table, report_sources, technical_table],
            )

        with gr.Tab("Data notes"):
            gr.Markdown(_data_notes(manifest, quality))
            with gr.Accordion("Technical manifest", open=False):
                gr.JSON(manifest)
            with gr.Accordion("Quality report", open=False):
                gr.JSON(quality)
    return app